NEW APPROACHES TO PROTEIN STRUCTURE
PREDICTION AND DESIGN
Joe DeBartolo
An overview of my thesis
structure prediction
Why do prediction and design
matter?
amino
acid
sequence
Structure Prediction. Growth of sequences
outpaces experimental characterization.
Knowing their structure provides insights
into their function and interactions
Protein design. Understanding design
principles can allow the creation of new
proteins with therapeutic and industirial
applications
protein design
native
protein
structure
Protein structure prediction and design
PART I
PART II
PART III
PART IV
ItFix: Homology-free structure prediction
SPEED: ItFix enhanced with evolution
Future directions in prediction
Protein design
Protein structure prediction
1° structure
MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLR
local 2° structure
2° and 3° structure
topology diagram
3D model
Residue-residue contact map
The Challenge:
Distill the folding problem down to the basic principles,
code them into an algorithm, and predict pathways and
structure without using homology
…LEKVQLN…
amino acid
sequence
native
structure
Capturing the interrelated forces of protein structure
Ramachandran angles
local
structures
• backbone
hydrogen bonds
•
y
f
•
•
•
local sterics
solvation
backbone entropy
•
•
•
•
long range sterics
Van der Waals
electrostatics
hydrophobic effect
The overlapping features of local protein structure
turn
β-strand
α-helix
backbone 180
Ramachandran
torsion
ψ
angles
y
f
-180
-180
180
φ
-180
φ
180 -180
φ
backbone
H-bonds
polar
amphipathic
sidechain
patterning
polar
apolar
apolar
mostly polar
180
Capturing the interrelated forces of protein structure
•
•
y
f
•
•
ramachandran angles
backbone hydrogen bonds
solvation
long range effects
long-range hydrogen bonding
•
•
•
sterics
Van der Waals
electrostatics
3° packing specificity of the chain
hydrophobic effect
surface residue placement
solvent exposed
residues
salt bridges
and other
favorable
pairings
apolar buried residues
long-range hydrogen bonding
contacts
that are
highly
separated in
sequence
The structure prediction challenge:
To integrate all of these features into an algorithm
y
f
requirements
180
ψ
-180
-180

φ
a way to sample conformations
X
180
a way to evaluate conformations
Sample Ramachandran space
Rama angle pair
180
y
f
Rama map of PDB
ψ
Rama angle pairs describe entire
conformation...
NO sidechain rotamer sampling
-180
-180
exclude sidechains
beyond Cβ
φ
180
1° and 2° structure information refines the
Rama search space
180
Entire PDB
ψ
-180
-180
180
add amino acid identity
φ
ALL-ALL-ALL
ALL-ASN-ALL
ALL-ALL-ALL
ALL-ASN-GLY
ALL-ALL-ALL
180
ALL-ASN-GLY
ψ
-180
-180
ALL-ALL-ALL
180
ψ
-180
-180
180
add 2° structure identity
φ
2° structure
180
ψ
-180
-180
180
add neighbor identity
φ
1° structure
φ
180
BETA-ALL-ALL
The structure prediction challenge:
To integrate all of these features into one algorithm
y
f
requirements
180
ψ
-180
-180

φ
a way to sample conformations
X
180
a way to evaluate conformations
The DOPE statistical potential
Discrete Optimized Potential Energy
Knowledge-based modeling of the energy of a conformation
The DOPE atom pair energy…
residue j
residue i
amino acid i amino acid j
atom type j
atom type I
PDB
I have added to DOPE…
• orientation dependence
• 2° structure dependence
EnergyPDB
(rij) =biases
-ln( ProbPDB(rij) )
• eliminate
local
rij is the distance between atoms i and j
Shen and Sali, Proteins (2007)
GLU-Cβ - GLU-Cβ
LEU-Cβ - LEU-Cβ
Distance (Å)
DOPE-PW
DOPE PW energy
DOPE energy
DOPE
GLU-Cβ - GLU-Cβ
LEU-Cβ - LEU-Cβ
Distance (Å)
Capturing sidechain orientation in a sidechain-free model
PW = r =
( r1 2  90)  ( r 21  90)
2
2
ρ1-2 is the angle
between two
vectors
High
low ρρ (in-line)
residue 1
Ca
residueρ1
1-2
Cβ
Cβ
Cb
ρ1-2 Ca ρ2-1
Cβ
Ca
residue 2
ρ2-1
,
Cα
Cβ
Ca
residue 2
DeBartolo et al. PNAS 2009
DOPE-PW (uniquely) captures the hydrophobic effect
Potential orientations of high PW
DOPE energy
hydrophobic residues
pairs have lower
buried in
energy at smaller the core
distances
GLU-Cβ  GLU-Cβ
LEU-Cβ LEU-Cβ
Cα
Cβ
Cα
Cβ
Cβ
Cα
Cα
Cb
Cα
Cβ
Cα
Cβ
Distance (Å)
large distance preferred
DOPE-PW captures the amphipathic nature of β-sheets
DOPE energy
polar and apolar
residues prefer
opposing sides of the
β-sheet
potential orientations of low PW
same side
of β-sheet
Cβ
Cβ
Cα
Cα
GLU-Cβ  LYS-Cβ
GLU-Cβ  LEU-Cβ
opposite
side of βsheet
Cβ
Cα
Cα
Distance (Å)
Cβ
The challenge:
To integrate all of these features into one algorithm
y
f
requirements
180
ψ
-180
-180

φ
a way to sample conformations
X
180
a way to evaluate conformations
ItFix
Iterative Fixing to reduce the conformational search
sampling
library
Fold with (f,y) from
“I1”
LibraryInitial
Fold with (f,y) from LibraryRestricted 1
“I2”
Remove trimers of lowlypopulated 2o structure
Fold with (f,y) from LibraryRestricted 2
Remove trimers
Repeat until no further fixing is possible
Final
Round
Fold with (f,y) from LibraryRestricted final
Repeat removal
“N”
helix
strand
Not(Strand)
Not(Helix)
Coil subtypes
search space is restricted
“U”
180°
2° structure option removed
Starting configuration 1° only
(no 2o structure restriction)
ψ
-180°
180°
ψ
-180°
180°
ψ
-180°
-180°
φ
180°
DeBartolo et al., PNAS 2009
Homology-free ItFix
2° and 3° structure prediction results
Native
ItFix
SSPro
PSIPRED
---HHHHHHHHHHHHHHH-----GGGHHHHHHHHHHHHHHHT---HHHHHHHHHH-TT-THHHHHHHH---HHHHHHHHHHHHHHHT-----S-HHHHHHHHHHHHHHHT-S--HHHHHHHHHT---HHHHHHHHH---HHHHHHHHHHHHHHHHHHE-TTHHHHHHHHHHHHHHHHT--HHHHHHHHHHT-TTHHHHHHHHHH---HHHHHHHHHHHHHHH-----HHHHHHHHHHHHHHHHHH----HHHHHHHHH----HHHHHHHHH--
1af7 2.5 Å
Native
ItFix
SSPro
PSIPRED
-HHHHHHHHHHHTT-SS--HHHHHHHHHHHT--HHHHHHHHHHHHHHHH--HHHHHHHHHHHH-----HHHHHHHHHHHH--S-HHHHHHHHHHHHHH-HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH-HHHHEEHEHHHHHHH--HHHHHHHHHHHHH-----HHHHHHHHHHHHHHHHHHHHHHH-HHHH---
1b72 1.6 Å
Native
ItFix
SSPro
PsiPred
-EEEEEEEEETTTTEEEEE-TTS--EEEEGGGB-SSSS----TT-EEEEEEEEETTEEEEEEEEE--EEEEEEEE-STTTEEEEEEET-T-EEEEEEE--SSS-----TS--EEEEEEES--S----EEEEE--TEEEEEE-TTTTEEEE--TT--EEEEEEEHEETTT--E--TT-EEEEEEEE-TT--E-EE------EEEEEEEE----EEEEE-----EEEEEEE--------------EEEEEEEE-----EEEEEE---
1csp 6.0 Å
Native
ItFix
SSPro
PSIPRED
--BGGG---SEEEEE-TTS-EEEEEEHHHHHHHHHHTT-EEEEEETTSSS-EEEEE-EEE-SSSSEEEEEE-TTS-EEEEEEHHHHHHHHHHHT--EEEE-TTSSS-EEEEE--BBTEEE-EEEEEEETTT-EEEEE-HHHHHHHHHHHT--EEEE-TT----EEEE-----------EEEEE-----EEEEE-HHHHHHHHHH----EEEE-------EEEE--
1tif 4.2 Å
Native
ItFix
SSPro
PsiPred
-HHHHHHHHHHHTT--HHHHHHHHTS-HHHHHHHHTTS-SS-TTHHHHHHHTT--HHHHH-HHHHHHHHHHHHT--HHHHHHHHT--HHHHHHHHTT--SS----HHHHHHHT--HHHHH---HHHHHHHHHHHHHHHHHHHHHT-HHHHHHHHHTT-------HHHHHHHHHT--HHHH-HHHHHHHHHHH----HHHHHHHH---HHHHHHHH------HHHHHHHHHHH---HHHH--
1r69 2.4 Å
Native
ItFix
SSPro
PSIPRED
-EEEEEETTS-EEEEE--TTSBHHHHHHHHHHHH---GGGEEEEETTEE--TTSBTGGGT--TT-EEEEEE-EEEEEETTS-EEEEEE---S-B-HHHHHHHHHSS---SSEEEEETT----TT-B----------EEEEEE-EEEEEEETTEEEEEEE---SHHHHHHHHHHHTTT---T--E--ETT-E--TT-EEEEEE--TT-EEEEEE-EEEEEE----EEEEEE-----HHHHHHHHHHHH---HHHEEEEE--EE------HHH-------EEEEEE-
1ubq 3.1 Å
DeBartolo et al., PNAS 2009
1
b1
b2
helix
b4 b5
310
Major pathway
(from experiment)
b3
Unfolded
state
10
Round 0
b1-b2
hairpin
+ b3
+helix
10
Round 2
+ b4
1 0
+ b3
Round 3
1 0
+helix
+ b4
Round 4
1 0
+310
helix
Round 6
10
+ b5
Round 9
b1
b2
helix
b4 b5
0
2° Structure frequency
Round 1
Mimicking
folding
pathways
1
residue index
310
b3
73
Native
state
DeBartolo et al., PNAS 2009
Part I Conclusions
Challenge:
Distill the folding problem down to the basic principles,
code them into an algorithm, and predict pathways and
structure without using homology
What novel about how we approached this challenge?
Use basic principles of protein structure and folding.
Search strategies: mimic true folding behavior
i)
Coupled 2° & 3° structure formation
ii)
Iterative fixing to reduce the search
iii)
Outputs pathway information
Energy functions: orientational and 2° structure dependence
Protein structure prediction and design
 PART I
PART II
PART III
PART IV
ItFix: Homology-free structure prediction
SPEED: ItFix enhanced with evolution
Future directions in prediction
Protein design
ψ
φ
Cover image of Protein Science, March 2010
SPEED: Structure Prediction Enhanced by Evolutionary Diversity
Increase φ, ψ diversity and accuracy
multiple sequence
alignment
target sequence
sequence
database
MQIFVKTLTGKTITLEV
180°
ψ
180°
-180°
-180°
φ
homology-free
sampling
180°
ψ
SPEED
sampling
-180°
-180°
φ
180°
IEIKIRDIYSKTYKFMA
IEITCNDRLGKKVRVKC
MRLFIRSHLHDQVVISA
MKLSVKSPNGRIEIFNE
LQFFVRLLDGKSVTLTF
IEITLNDRLGKKIRVKC
IEIWVNDHLSHRERIKC
MDVFLMIRRQKTTIFDA
IIVTVNDRLGTKAQIPA
MRISVIKLDSTSFDVAV
MNVNFRTILGKTYTITV
MLLTVRDRSELTFSLQV
MQIFVTTPSENVFGLEV
MSLTIKF-GAKSIALSL
MKYRIRTISNDEAVIEL
… ~1000 sequences
Uses sequence data base 107 seq’s, growing fast;
PDB only 104 structures growing slowly
ItFix-SPEED overview
Homology-free
1tif position 4
…AGTYEFRKAKIT…
homology
free
Multiple
Sequence
Alignment
INE
SPEED
1tif position 4
{IND , IGD , VGN,…}MSA
180°
SPEED
Round 1 Rama
distribution ψ
Rama Distribution
Fold 500x
with Eradial
-180°
ItFix
180°
Analyze 2° Structure Statistics
no
Round 2 Rama ψ
distribution
2° structure
converged
yes
-180°
180°
Final 2° Structure
Fold 10000x with Eradial
or DOPE-PW (all α)
Final Rama
distribution
ψ
-180°
-180°
φ
180° -180°
φ
180°
DeBartolo et al., Protein Sci. 2010
ItFix-SPEED overview
Homology-free
1tif position 4
…AGTYEFRKAKIT…
homology
free
Multiple
Sequence
Alignment
INE
SPEED
1tif position 4
{IND , IGD , VGN,…}MSA
180°
SPEED
Round 1 Rama
distribution ψ
Rama Distribution
Fold 500x
with Eradial
-180°
ItFix
180°
Analyze 2° Structure Statistics
no
Round 2 Rama ψ
distribution
2° structure
converged
yes
-180°
180°
Final 2° Structure
Fold 10000x with Eradial
or DOPE-PW (all α)
Final Rama
distribution
ψ
cluster
Largest
cluster
Refine 100X each
with DOPE-PW
Reject ∆Eradial> 0
-180°
-180°
φ
180° -180°
φ
180°
prediction
min<Energy> 100
DeBartolo et al., Protein Sci. 2010
Assaying accuracy
Clustering predicts model accuracy and confidence
Mean Ca-RMSD to native of cluster (Å)
fold ItFix
predicted 2°
structure
cluster
identify best cluster
Global Accuracy
8
Local Accuracy
1af7
2
7
R =0.85
6
5
1b72
4
3
1r69
2
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Mean Ca-RMSD between models in cluster (Å)
(i.e.
we know whether we got it right or wrong)
Cut-off Distance (Å)
Performance
in CASP8
Global Distance Test
T0482 (4.8 Å)
ItFix
free
modeling
Cut-off Distance (Å)
T0405 D1 (6.4 Å )
ItFix
T0464 D1 (4.5 Å)
Cut-off Distance (Å)
loop insertion
modeling
RAPTOR
ItFix
Aashish Adhikari
ItFix
DeBartolo et al.,
Protein Sci. 2010
Better
template
Cut-off Distance (Å)
T0429 D2 (6.8 Å)
RAPTOR
ItFix
Percentage of residues
template
identification
using folding
Part II
Conclusions
• Adding evolutionary information to ItFix improves the
accuracy of the conformational search
• Clustering permits global and local prediction of cluster
accuracy and uncertainty
• SPEED is successful in the CASP8 experiment
Protein structure prediction and design
 PART I
 PART II
 PART III
PART IV
ItFix: Homology-free structure prediction
SPEED: ItFix enhanced with evolution
Future directions in prediction
Protein design
1° structure
MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLR
Invert the structure prediction problem
local 2° structure
2° and 3° structure
topology diagram
3D model
3D contacts
1° structure
MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLR
Current designs are very similar to parent sequences
design
length
fold
wt % id
(wt % sim)
top % id
(top % sim)
top-wt % id
(top-wt % sim)
protein L1
62
αβ
35 (61)
50 (62)
73 (86)
protein L2
62
αβ
45 (60)
45 (60)
73 (86)
ACP
98
αβ
41 (54)
39 (57)
67 (69)
PCP
S6
U1A
FKB
70
94
96
107
αβ
αβ
αβ
αβ
31 (56)
26 (43)
32 (57)
42 (59)
33 (56)
32 (46)
33 (57)
44 (62)
73 (84)
33 (52)
97 (100)
96 (96)
zinc-finger
28
αβ
21 (38)
N/A
N/A
tenascin
89
β
42 (64)
42 (64)
100 (100)
Can we design a more unique protein sequence?
Design method
01010111
1
Restrict AA possibilities
by burial in native
structure for the
hydrophobic effect
2
Find best sequences for
maximum Rama
propensity
3
Monte Carlo search of
Statistical Potential
DOPE PW energy
DOPE-PW
GLU-Cβ - GLU-Cβ
LEU-Cβ - LEU-Cβ
Distance (Å)
MKLFVKTP…
LTVTIR L
IV R E
positional
sequence
library
Hello Jello
Preliminary wetlab analysis
cd
• 1ds0 expresses in inclusion bodies
• mutations enhance in vitro solubility
• further experiments needed
design
design-sol
wavelength (nm)
native
Thesis defense
Conclusions
• Homology-free structure prediction can provide
accurate models by mimicking folding pathways
• Adding evolutionary information improves the accuracy
of the conformational search
• Inverting our homology-free prediction method into
a design algorithm aims to generate unique amino
acid sequences
Acknowledgements
Prof. Tobin Sosnick
Prof. Karl Freed
Prof. Jinbo Xu
Glen Hocky
Andres Colubri
James Fitzgerald
Abhishek Jha
Esmael Haddadian
James Hinshaw
Aashish Adhikari
Jouko Virtanen
Chloe Antoniou
Josiah Zayner
Feng Zhao
Jian Peng
Grzegorz Gawlak
Srikanth Aravamuthan
Funding: NIH, NSF, Joint Theory Institute
Native Rama probability
Enhancement of Ramachandran propensity
ψ
φ
AA
SecStr
position
Enhancement in energy and structure prediction
•
•
∆∆E = -120 (arb. units)
2X enhancement in native-like models in prediction
1b72
1.6 Å
1di2 4.6 Å
1r69
2.4 Å
1
1af7
2.7 Å
Round 0
Round 0
Round 0
Round 1
Round 1
Round 1
Round 1
Round 2
Round 2
Round 2
Round 2
Round 3
Round 3
Round 4
Round 3
Round 5
Round 4
Round 6
Round 4
Round 7
Round 6
Round 8
Round 6
10
10
1 0
10
0
Secondary Structure frequency
10
Round 0
residue index
SPEED increases the native Rama probability
native Rama regions
180
2
ψ
1
-180
-180
Native basin probability
1b72
φ
% positions with PNative > 0.25
3
SPEED reduces cases
SPEED
whereimproves
native φ,native
ψ hasφ,
aψ
probability
sequence
very lowacross
probability
4
180
2° structure by position
PDB
of target
Amino acid
by id
position
Radial energy terms enforce productive chain collapse
(global terms)
Rg-Cα: Root-squared
distance of Cα from CM.
Compactness of model
Rg-phil
Rg-phob
CMCα
Rg-Cα
Cα
Cβ
Ru-Cα: Root-meansquared deviation of Cα
from CM. Enforces a
spherical model
Rg-phob/Rg-phil (burial
ratio): best packing of
hydrophobic residues
Eliminating
the fixing
thresholds
from ItFix
180
(e.g. pos. 67)
MQIFVKT…STLHLVLR
round0
Rama distribution
0
-180
180
fold 2000X
round1
Rama distribution
0
-180
180
fold 2000X
round2
Rama distribution
0
-180
180
fold 2000X
round3
Rama distribution
0
-180
-180
0
180
An evolution-enhanced energy function
DOPE-PW-SPEED
10
WT:ILE
Homologs: polar
DOPE-PW
DOPE-PW-SPEED
WT:Ala
Homologs: polar
energy
8
6
4
2
0
0.0
10
PHE4
THR14
energy
8
5.0
10.0
15.0
20.0
25.0
30.0
distance (Å)
DOPE-PW
DOPE-PW-SPEED
6
4
2
0
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0
distance (Å)